CUED Publications database

Sequential inference methods for non-homogeneous poisson processes with state-space prior

Li, C and Godsill, SJ (2018) Sequential inference methods for non-homogeneous poisson processes with state-space prior. In: UNSPECIFIED pp. 2856-2860..

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Abstract

© 2018 IEEE. The Non-homogeneous Poisson process is a point process with time-varying intensity across its domain, the use of which arises in numerous areas in signal processing and machine learning. However, applications are largely limited by the intractable likelihood function and the high computational cost of existing inference schemes. We present a sequential inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm to enable online inference in various applications. The proposed model is compared to competing methods on synthetic datasets and tested with real-world financial data.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 03 Jan 2019 01:19
Last Modified: 19 Sep 2019 03:48
DOI: 10.1109/ICASSP.2018.8462457